🏆 Data Feeling | AIeron
IT предприниматель и препод 🧑🏫 ex-ML лидер в Dodo Brands 🦤🍕 Прокачиваю людей в Data Science 🚀 Победитель Stepik Awards 🏆 Kaggle Expert 🤹♀️ Создатель @Speakadora_bot @big_llm_course РКН https://clik.now/datafeeling Алерон @Ale_v2
Show more📈 Analytical overview of Telegram channel 🏆 Data Feeling | AIeron
Channel 🏆 Data Feeling | AIeron (@datafeeling) in the Russian language segment is an active participant. Currently, the community unites 14 706 subscribers, ranking 719 in the Marketing & PR category and 45 433 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 706 subscribers.
According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -74 over the last 30 days and by -5 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 21.73%. Within the first 24 hours after publication, content typically collects 7.27% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 196 views. Within the first day, a publication typically gains 1 070 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 47.
- Thematic interests: Content is focused on key topics such as лот, n8n, бразилия, пет, санкция.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“IT предприниматель и препод 🧑🏫
ex-ML лидер в Dodo Brands 🦤🍕
Прокачиваю людей в Data Science 🚀
Победитель Stepik Awards 🏆
Kaggle Expert 🤹♀️
Создатель @Speakadora_bot @big_llm_course
РКН https://clik.now/datafeeling
Алерон @Ale_v2”
Thanks to the high frequency of updates (latest data received on 15 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Marketing & PR category.
◼️15:35 Вводный доклад про Optimal Transport — Александр Коротин, AIRI, Сколтех ◼️15:45 Optimal Flow Matching: Learning Straight Trajectories in Just One Step — Никита Корнилов, МФТИ, Сколтех ◼️16:05 Adversarial Schrödinger Bridge Matching — Даниил Селиханович, Сколтех ◼️16:25 Light Unbalanced Optimal Transport — Милена Газдиева, Сколтех ◼️16:45 Rethinking Optimal Transport in Offline Reinforcement Learning — Арип Асадулаев, AIRI, МФТИ, ИТМО ◼️17:05 Energy-Guided Continuous Entropic Barycenter Estimation for General Costs — Александр Колесов, Сколтех ◼️17:25 ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport — Назар Бузун, AIRI ◼️17:45 On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity — Александр Тюрин, AIRI ◼️19:05 Group and Shuffle: Efficient Structured Orthogonal Parametrization — Михаил Горбунов, EPFL6 декабря: YouTube, VK Bидео
◼️15:35 ∇2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials — Кузьма Храбров, AIRI ◼️15:55 XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX — Александр Никулин, AIRI ◼️16:15 BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack — Юрий Куратов, AIRI, МФТИ ◼️16:35 RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation — Антон Антонов, AIRI ◼️16:55 HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach — Максим Николаев, AIRI ◼️17:15 EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas — Михаил Мозиков, AIRI, МИСИСВедущий ИИшницы — Артур Кадурин, AIRI. До встречи!
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